Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A new concept for separability problems in blind source separation
Neural Computation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Linear dimension reduction based on the fourth-order cumulant tensor
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Estimating non-gaussian subspaces by characteristic functions
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Joint low-rank approximation for extracting non-Gaussian subspaces
Signal Processing
Independent subspace analysis is unique, given irreducibility
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Colored subspace analysis: dimension reduction based on a signal's autocorrelation structure
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Estimating non-gaussian subspaces by characteristic functions
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Contrast functions for independent subspace analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Uniqueness of linear factorizations into independent subspaces
Journal of Multivariate Analysis
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Dimension reduction provides an important tool for preprocessing large scale data sets. A possible model for dimension reduction is realized by projecting onto the non-Gaussian part of a given multivariate recording. We prove that the subspaces of such a projection are unique given that the Gaussian subspace is of maximal dimension. This result therefore guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals.